World Population

This data was retrieved Worldometers

world_pop %>% 
  janitor::clean_names() %>% 
  mutate_all(~str_remove_all(., pattern = "%|[:space:]")) %>% 
  mutate_all(~parse_guess(.)) %>%
  mutate(year = paste0(year, "-01-01"),
         year = as.Date(year)) %>% 
  drop_na() %>% 
  rename(date = year, pop = 2, percent_change = 3, density = 5, yearly_change = 4) -> df

ggplot2::theme_set(cowplot::theme_cowplot(font_size = 18))
df %>% 
  arrange(date) %>%
  mutate(rank = dense_rank(yearly_change)) %>% 
  arrange(desc(rank)) %>% 
  arrange(date) %>% 
  mutate_at(.vars = c(2:4), as.double) -> df_1

df_1 %>%
  select(1:4) %>% 
  mutate_at(.vars = c(2,4), scales::comma) %>% 
  mutate(date = format(x = date, "%Y"),
         percent_change = percent_change/100,
         percent_change = scales::percent(percent_change, accuracy = 0.01)) %>%
  rename(Year = date, 'World Population' = pop, 'Percentage Change' = percent_change, 'Yearly Change' = yearly_change) %>% 
  DT::datatable(rownames = F)

country_pop %>% 
  janitor::clean_names() %>% 
  mutate_all(~str_remove_all(., pattern = "%|[:space:]")) %>% 
  mutate_all(~parse_guess(.)) %>%
  drop_na() %>%
  rename(Rank = number, Country = 2, Population = 3, density = 5, yearly_change = 4) %>% 
  select(1:4) %>% 
  mutate_at(.vars = c(3), scales::comma) %>% 
  mutate(yearly_change = paste0(yearly_change, "%")) %>%
  rename('Yearly Change' = yearly_change) %>% 
  DT::datatable(rownames = F,
                editable = T,
                style = "bootstrap5")

Plots

 # Plot 1 -> Percentage Increase
  p1 <- df_1 %>% 
  ggplot(aes(x = date, y = percent_change, fill = percent_change)) +
  geom_bar(stat = "identity", color = "black", show.legend = F)+
  scale_x_date(date_breaks = "10 years", 
               date_labels = "%Y",
               limits = c(as.Date("1950-01-01"), as.Date("2024-01-01")), 
               expand = c(0,1)) +
  scale_y_continuous(labels = scales::percent_format(scale = 1)) +
  labs(x = "", y = "") +
  ggtitle("World Yearly Growth") +
  scale_fill_viridis_c(direction = -1) +
  theme(plot.title = element_text(face = "bold", family = "mono", size = 28, color = "#1e0628", hjust = .5), 
        panel.grid.major.y = element_line(colour = "black"))
  
# Plot 2 -> Population Increase
  p2 <- df_1 %>% 
  ggplot(aes(x = date, y = pop, fill = pop, 
             text = paste0("Pop: ", scales::comma(pop, accuracy = 1)))) +
  geom_bar(stat = "identity", color = "black", show.legend = F) +
  scale_x_date(date_breaks = "10 years", 
               date_labels = "%Y",
               limits = c(as.Date("1950-01-01"), as.Date("2024-01-01")), 
               expand = c(0,1)) +
  scale_y_continuous(labels = scales::number_format(scale = .000000001, suffix = " B")) +
  labs(x = "", y = "") +
  ggtitle("World Population") +
  scale_fill_viridis_c(option = "inferno", direction = -1) +
  theme(plot.title = element_text(face = "bold", family = "mono", size = 28, color = "#1e0628", hjust = .5), 
        panel.grid.major.y = element_line(colour = "black"))

# Plotly
  plotly::ggplotly(p1, tooltip = c("x", "y")) -> p1_plotly
  plotly::ggplotly(p2,tooltip = c("x","text")) -> p2_plotly
  crosstalk::bscols(widths = c(12, 12), p1_plotly, p2_plotly) -> world_pop
  
world_pop